


import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference







class 							lowerCamelCase__						(				UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Dict 			=		ShapEImgaImgPipeline
			_UpperCamelCase		:       Optional[Any] 			=		['image']
			_UpperCamelCase		:       Optional[Any] 			=		['image']
			_UpperCamelCase		:       List[Any] 			=		[
			    'num_images_per_prompt',
			    'num_inference_steps',
			    'generator',
			    'latents',
			    'guidance_scale',
			    'frame_size',
			    'output_type',
			    'return_dict',
			]
			_UpperCamelCase		:       Dict 			=		False




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return 32




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return 32




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.time_input_dim * 4




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return 8




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						torch.manual_seed(0  )
						UpperCamelCase__       =							CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size   ,    image_size=64   ,    projection_dim=self.text_embedder_hidden_size   ,    intermediate_size=37   ,    num_attention_heads=4   ,    num_channels=3   ,    num_hidden_layers=5   ,    patch_size=1   ,    )

						UpperCamelCase__       =							CLIPVisionModel(snake_case  )
						return model




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							CLIPImageProcessor(
						    crop_size=224   ,    do_center_crop=snake_case   ,    do_normalize=snake_case   ,    do_resize=snake_case   ,    image_mean=[0.48145466, 0.4578275, 0.40821073]   ,    image_std=[0.26862954, 0.26130258, 0.27577711]   ,    resample=3   ,    size=224   ,    )

						return image_processor




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						torch.manual_seed(0  )

						UpperCamelCase__       =							{
						    "num_attention_heads": 2,
						    "attention_head_dim": 16,
						    "embedding_dim": self.time_input_dim,
						    "num_embeddings": 32,
						    "embedding_proj_dim": self.text_embedder_hidden_size,
						    "time_embed_dim": self.time_embed_dim,
						    "num_layers": 1,
						    "clip_embed_dim": self.time_input_dim * 2,
						    "additional_embeddings": 0,
						    "time_embed_act_fn": "gelu",
						    "norm_in_type": "layer",
						    "embedding_proj_norm_type": "layer",
						    "encoder_hid_proj_type": None,
						    "added_emb_type": None,
						}

						UpperCamelCase__       =							PriorTransformer(**snake_case  )
						return model




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						torch.manual_seed(0  )

						UpperCamelCase__       =							{
						    "param_shapes": (
						        (self.renderer_dim, 93),
						        (self.renderer_dim, 8),
						        (self.renderer_dim, 8),
						        (self.renderer_dim, 8),
						    ),
						    "d_latent": self.time_input_dim,
						    "d_hidden": self.renderer_dim,
						    "n_output": 12,
						    "background": (
						        0.1,
						        0.1,
						        0.1,
						    ),
						}
						UpperCamelCase__       =							ShapERenderer(**snake_case  )
						return model




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.dummy_prior
						UpperCamelCase__       =							self.dummy_image_encoder
						UpperCamelCase__       =							self.dummy_image_processor
						UpperCamelCase__       =							self.dummy_renderer

						UpperCamelCase__       =							HeunDiscreteScheduler(
						    beta_schedule="exp"   ,    num_train_timesteps=1024   ,    prediction_type="sample"   ,    use_karras_sigmas=snake_case   ,    clip_sample=snake_case   ,    clip_sample_range=1.0   ,    )
						UpperCamelCase__       =							{
						    "prior": prior,
						    "image_encoder": image_encoder,
						    "image_processor": image_processor,
						    "renderer": renderer,
						    "scheduler": scheduler,
						}

						return components




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case=0  ):

						'''simple docstring'''


						UpperCamelCase__       =							floats_tensor((1, 3, 64, 64)   ,    rng=random.Random(snake_case  )  ).to(snake_case  )

						if str(snake_case  ).startswith("mps"  ):
									UpperCamelCase__       =							torch.manual_seed(snake_case  )
						else:
									UpperCamelCase__       =							torch.Generator(device=snake_case  ).manual_seed(snake_case  )
						UpperCamelCase__       =							{
						    "image": input_image,
						    "generator": generator,
						    "num_inference_steps": 1,
						    "frame_size": 32,
						    "output_type": "np",
						}
						return inputs




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							"cpu"

						UpperCamelCase__       =							self.get_dummy_components()

						UpperCamelCase__       =							self.pipeline_class(**snake_case  )
						UpperCamelCase__       =							pipe.to(snake_case  )

						pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							pipe(**self.get_dummy_inputs(snake_case  )  )
						UpperCamelCase__       =							output.images[0]
						UpperCamelCase__       =							image[0, -3:, -3:, -1]

						assert image.shape == (20, 32, 32, 3)

						UpperCamelCase__       =							np.array(
						    [
						        0.00039216,
						        0.00039216,
						        0.00039216,
						        0.00039216,
						        0.00039216,
						        0.00039216,
						        0.00039216,
						        0.00039216,
						        0.00039216,
						    ]  )

						assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self._test_inference_batch_consistent(batch_sizes=[1, 2]  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							torch_device == "cpu"
						UpperCamelCase__       =							True
						self._test_inference_batch_single_identical(
						    batch_size=2   ,    test_max_difference=snake_case   ,    relax_max_difference=snake_case   ,    )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_dummy_components()
						UpperCamelCase__       =							self.pipeline_class(**snake_case  )
						UpperCamelCase__       =							pipe.to(snake_case  )
						pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							1
						UpperCamelCase__       =							2

						UpperCamelCase__       =							self.get_dummy_inputs(snake_case  )

						for key in inputs.keys():
									if key in self.batch_params:
												UpperCamelCase__       =							batch_size * [inputs[key]]

						UpperCamelCase__       =							pipe(**snake_case   ,    num_images_per_prompt=snake_case  )[0]

						assert images.shape[0] == batch_size * num_images_per_prompt







@slow
@require_torch_gpu
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png"  )
						UpperCamelCase__       =							load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/shap_e/test_shap_e_img2img_out.npy"  )
						UpperCamelCase__       =							ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img"  )
						UpperCamelCase__       =							pipe.to(snake_case  )
						pipe.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							torch.Generator(device=snake_case  ).manual_seed(0  )

						UpperCamelCase__       =							pipe(
						    snake_case   ,    generator=snake_case   ,    guidance_scale=3.0   ,    num_inference_steps=64   ,    frame_size=64   ,    output_type="np"   ,    ).images[0]

						assert images.shape == (20, 64, 64, 3)

						assert_mean_pixel_difference(snake_case   ,    snake_case  )






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase     		=							logging.get_logger(__name__)

__UpperCamelCase     		=							{
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Dict 			=		'trocr'
			_UpperCamelCase		:       Union[str, Any] 			=		['past_key_values']
			_UpperCamelCase		:       Union[str, Any] 			=		{
			    'num_attention_heads': 'decoder_attention_heads',
			    'hidden_size': 'd_model',
			    'num_hidden_layers': 'decoder_layers',
			}




			def __init__(				self   ,    snake_case=50265   ,    snake_case=1024   ,    snake_case=12   ,    snake_case=16   ,    snake_case=4096   ,    snake_case="gelu"   ,    snake_case=512   ,    snake_case=0.1   ,    snake_case=0.0   ,    snake_case=0.0   ,    snake_case=2   ,    snake_case=0.02   ,    snake_case=0.0   ,    snake_case=True   ,    snake_case=False   ,    snake_case=True   ,    snake_case=True   ,    snake_case=1   ,    snake_case=0   ,    snake_case=2   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							vocab_size
						UpperCamelCase__       =							d_model
						UpperCamelCase__       =							decoder_layers
						UpperCamelCase__       =							decoder_attention_heads
						UpperCamelCase__       =							decoder_ffn_dim
						UpperCamelCase__       =							activation_function
						UpperCamelCase__       =							max_position_embeddings
						UpperCamelCase__       =							dropout
						UpperCamelCase__       =							attention_dropout
						UpperCamelCase__       =							activation_dropout
						UpperCamelCase__       =							init_std
						UpperCamelCase__       =							decoder_layerdrop
						UpperCamelCase__       =							use_cache
						UpperCamelCase__       =							scale_embedding
						UpperCamelCase__       =							use_learned_position_embeddings
						UpperCamelCase__       =							layernorm_embedding

						super().__init__(
						    pad_token_id=snake_case   ,    bos_token_id=snake_case   ,    eos_token_id=snake_case   ,    decoder_start_token_id=snake_case   ,    **snake_case   ,    )


